The paper "Generalized Encrypted Traffic Classification Using Inter-Flow Signals" has been accepted as a short paper at ARES 2025!

Brief summary

Network traffic analysis has long been a fundamental aspect of cybersecurity and network management. It plays a key role in a wide range of real-world applications, from securing enterprise networks and detecting malware to protecting user privacy. In this paper, we present a novel encrypted traffic classification model capable of operating directly on raw PCAP data without requiring prior assumptions about traffic type. Unlike existing methods, it is generalizable across multiple classification tasks and leverages inter-flow signals—an innovative representation that captures temporal correlations and packet volume distributions across flows. Experimental results show that our model outperforms well-established methods in nearly every classification task and across most datasets, achieving up to 99% accuracy in some cases, demonstrating its robustness and adaptability.